We train all models for 250 epochs and early stopping with a patience of 40 epochs. All models are trained using Adam Optimizer [1]} and the StepLR learning scheduler [2]} which decays the learning rate by a factor \(k\)  every \(N_{steps}\)  epochs. All models were trained on 5 random seeds \(\in \lbrace 5,10,21,42,2022\rbrace \) . We use Pytorch [2]} and Pytorch-Lightning [4]} for our implementations and we summarize all model's training hyper-parameters in Table REF .
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